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相关概念视频

Molecular Models02:00

Molecular Models

38.5K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
38.5K
Molecular Shapes01:18

Molecular Shapes

56.9K
Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...
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Fischer Projections02:18

Fischer Projections

13.3K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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Newman Projections02:06

Newman Projections

16.9K
Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
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Resonance and Hybrid Structures02:16

Resonance and Hybrid Structures

16.9K
According to the theory of resonance, if two or more Lewis structures with the same arrangement of atoms can be written for a molecule, ion, or radical, the actual distribution of electrons is an average of that shown by the various Lewis structures.
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.
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VSEPR Theory02:37

VSEPR Theory

9.5K
Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...
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相关实验视频

Updated: Jul 11, 2025

Modeling an Enzyme Active Site using Molecular Visualization Freeware
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Modeling an Enzyme Active Site using Molecular Visualization Freeware

Published on: December 25, 2021

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化学结构感知分子图像表示学习化学结构感知分子图像表示学习

Hongxin Xiang1,2, Shuting Jin3,4,2, Xiangrong Liu4,5

  • 1School of Information Science and Engineering, Hunan University, Hunan, 410082, China.

Briefings in bioinformatics
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于药物发现中的分子图像分析的新方法,使计算机能够从没有标签的图像中理解化学结构. 这种方法显著改善了分子表示学习和药物发现过程.

关键词:
相反的学习学习学习.卷积中性网络的卷积中性网络.发现药物的发现.图形神经网络的神经网络没有监督的学习学习.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 药物发现 药物发现

背景情况:

  • 基于分子图像的药物发现面临着未标记的数据和从图像中提取化学结构的挑战.
  • 目前的方法很难弥合隐性图像信息和显式化学结构编码之间的差距.

研究的目的:

  • 开发一种用于分子表示学习的新框架,有效地将化学知识从分子图表转移到图像.
  • 为了使分子图像编码器能够在没有明确标签的情况下感知和解释化学结构.

主要方法:

  • 提出了一个对比图像预训练 (CGIP) 框架,利用自我监督的对比学习.
  • 采用内部和跨模式的对比学习来从大规模的未标记分子中提取显式图形信息和隐式图像信息.
  • 杆分子图表编码明确的化学结构 (例如,环,双键).

主要成果:

  • 在12个基准数据集中,在分子性质预测,跨模式检索和分布相似性任务中实现了最先进的性能.
  • 证明成功地将化学知识从图表转移到图像,允许图像编码器识别化学结构.
  • 验证了框架在从未标记的分子数据中学习的有效性.

结论:

  • 对比图形图像预训 (CGIP) 框架有效地弥合了分子图形和图像之间的差距,用于表示学习.
  • CGIP使图像编码器能够解释化学结构,从而推进基于分子图像的药物发现.
  • 这种方法突出了利用分子图像在化学中增强代表性学习的潜力.